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Modelling and Estimating Large Macroeconomic Shocks During the Pandemic

Luisa Corrado, Stefano Grassi () and Aldo Paolillo ()

No 530, National Institute of Economic and Social Research (NIESR) Discussion Papers from National Institute of Economic and Social Research

Abstract: This paper proposes and estimates a new Two-Sector One-Agent model that features large shocks. The resulting medium-scale New Keynesian model includes the standard real and nominal frictions used in the empirical literature and allows for heterogeneous COVID-19 pandemic exposure across sectors. We solve the model nonlinearly and we propose a new nonlinear, non-Gaussian filter designed to handle large pandemic shocks to make inference feasible. Monte Carlo experiments show that it correctly identifies the source and time location of shocks with a massively reduced running time, making the estimation of macro-models with disaster shocks feasible. The estimation is carried out using the Sequential Monte Carlo sampler recently proposed by Herbst and Schorfheide (2014). Our empirical results show that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. More precisely, starting from the second quarter of 2020, the model detects the occurrence of a large negative demand shock in consuming all kinds of goods, together with a large negative demand shock in consuming contact-intensive products. On the supply side, our proposed method detects a large labor supply shock to the general sector and a large labor productivity shock in the pandemic-sensitive sector.

Keywords: COVID-19; Nonlinear; Non-Gaussian; Large shocks; DSGE (search for similar items in EconPapers)
JEL-codes: C11 C51 E30 (search for similar items in EconPapers)
Date: 2021-06
New Economics Papers: this item is included in nep-cwa, nep-dge, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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